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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Vivek Parashar Megha Singh Senthamarai. M P. Poonkuzhali Parashuram Shankar Vadar B. Swapna School of Computing Science and Engineering VIT Bhopal University Bhopal Department of Computer Science and Engineering Oriental university Indore Department of Artificial Intelligence and Data Science Nandha Engineering College Erode TamilNadu Department of ECE R.M.D. Engineering College Kavaraipettai Yashwantrao Chavan School of Rural Development Shivaji University Kolhapur Maharashtra India Dr MGR Educational and Research Institute Chennai

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Innovative solutions in many fields, including agriculture, have been made possible by the widespread use of Internet of Things (IoT) technology. Our goal in this research was to increase crop yields by developing a smart irrigation system that would use the IoT. An ESP8266 Wi-Fi-enabled controller serves as the center of the system, collecting data and making decisions all at once. Important parts include sensors for soil moisture, air temperature and humidity, light intensity, water level, a pump to move water, and the ability to connect to a cloud service for data storage and analysis. Water level, soil moisture, temperature, humidity, and light intensity are some of the environmental characteristics that must be continuously monitored in order for the system to function. The data is sent to the main controller in real-time by the sensors using Wi-Fi. Based on predefined thresholds and algorithms, the system dynamically adjusts irrigation schedules to optimize water usage and enhance crop growth. To evaluate the system's performance, sensor readings from multiple trials were analyzed. Results indicate that the system effectively maintains optimal environmental conditions for plant growth, leading to improved agricultural productivity. The integration with a cloud platform enables remote monitoring and control, facilitating efficient water resource management and decision-making for farmers.

关键词： Temperature sensors Productivity Irrigation Cloud computing Soil moisture Humidity Sensor phenomena and characterization Internet of Things Wireless fidelity Water resources

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Book Recommendation System Using Hybrid Content and Collaborative Filtering Techniques

Book Recommendation System Using Hybrid Content and Collabor...

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Disruptive Technologies (ICDT), International Conference on

作者： Urvi Gupta Tripti Singh Vidyush Singh Umang Rastogi Sushil Kumar Computer Science and Engineering KIET Group of Institutions Delhi-NCR Ghaziabad UP India CSE Department KIET Group of Institutions Delhi-NCR Ghaziabad India CSE Department Data Science & Deep Learning Lab KIET Group of Institutions Delhi-NCR Ghaziabad India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

In this study, the process of producing a book recommendation system outlines the process of finding books to provide personalized suggestions according to user preference and behavior. The system increases user satisfaction by tailoring recommendations to their tastes, improving the overall reading experience, and enhancing engagement and retention on plat-forms. It tracks users with collaborative filtering. interactions, content-based filtering to study book characteristics such as genre and author, and mixed methods to combine both techniques that will help ensure accuracy and circumvent similar limitations of cold start problems. Machine learning models-, pre-processing of data algorithms, and accuracy and recall measures of analysis ensure the effectiveness of the system in providing individualized and relevant recommendations that are 85% accurate. The findings reveal test the system's capacity to generate good suggestions, improving user experience and engagement. Improvements in the future could include real-time recommendations and broader feedback integration towards further optimization in accuracy and usability.

关键词： Measurement Accuracy Filtering Collaborative filtering User experience Real-time systems User preference Usability Recommender systems Optimization

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Efficient Way of Heart Disease Prediction and Analysis using different Ensemble Algorithm: A Comparative Study

Efficient Way of Heart Disease Prediction and Analysis using...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Premkumar Duraisamy Yuvaraj Natarajan Ebin N L Jawahar Raja P Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9781665482721

This work presents a variety of voting ensemble models to measure human heart disease. This work aims to give confidence to doctors to diagnose heart disease accurately. S o that they can begin treatment in an earlier and more effective manner, thereby saving a valuable human life. The dataset can be trained by different models like Voting Ensemble, Light GBM, Stack Ensemble, XG Boost Classifier, Random Forest, and Logistic Regression. The work considered the 12 attributes and trained the model with different algorithms. Finally, the result shows that the systolic blood pressure parameter is the primary reason for heart disease compared to any other parameter in the data set. Considering all the different algorithms, the voting ensemble method produced more than 80 % accuracy.

关键词： Heart Machine learning algorithms Medical services Prediction methods Prediction algorithms Classification algorithms Ensemble learning

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RefXVC: Cross-Lingual Voice Conversion with Enhanced Reference Leveraging

arXiv

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arXiv 2024年

作者： Zhang, Mingyang Zhou, Yi Ren, Yi Zhang, Chen Yin, Xiang Li, Haizhou The Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China The Department of Electrical and Computer Engineering National University of Singapore Singapore Speech & Audio Team ByteDance AI Lab

This paper proposes RefXVC, a method for cross-lingual voice conversion (XVC) that leverages reference information to improve conversion performance. Previous XVC works generally take an average speaker embedding to condition the speaker identity, which does not account for the changing timbre of speech that occurs with different pronunciations. To address this, our method uses both global and local speaker embeddings to capture the timbre changes during speech conversion. Additionally, we observed a connection between timbre and pronunciation in different languages and utilized this by incorporating a timbre encoder and a pronunciation matching network into our model. Furthermore, we found that the variation in tones is not adequately reflected in a sentence, and therefore, we used multiple references to better capture the range of a speaker’s voice. The proposed method outperformed existing systems in terms of both speech quality and speaker similarity, highlighting the effectiveness of leveraging reference information in cross-lingual voice conversion. The converted speech samples can be found on the website: http://*** © 2024, CC BY-NC-SA.

关键词： Embeddings

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Plant Disease Classification Using VGG-19 Based Faster-RCNN 7th

Plant Disease Classification Using VGG-19 Based Faster-RCNN

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Proceedings of the 7th International Conference on Advances in Computing and data sciences, ICACDS 2023

作者： Nawaz, Marriam Nazir, Tahira Khan, Muhammad Attique Rajinikanth, Venkatesan Kadry, Seifedine Department of Software Engineering University of Engineering and Technology Taxila Taxila47050 Pakistan Faculty of Computing Department of Computer Science Riphah International University Gulberg Green Campus Islamabad Pakistan Department of Computer Science HITEC University Taxila Pakistan Department of Computer Science and Engineering Division of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Department of Applied Data Science Noroff University College Kristiansand4612 Norway Ajman University Ajman346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

ISBN: (纸本)9783031379390

Early plant disease diagnosis can help farmers avoid spending money on costly crop pesticides and aid them to boost food production. Scientists have put a lot of effort to classify plant diseases, but it is difficult to quickly locate and identify different crop abnormalities due to the high degree of resemblance between the normal and damaged parts of plant leaves. Additionally, the procedure of detecting plant diseases has been made more challenging due to the extensive color, size, shape, and intensity variations in the background and foreground of the plant images. To address the existing difficulties, we have introduced an effective deep learning (DL) based system called Faster-RCNN o recognize and classify various types of plant diseases. The suggested method consists of 3 basic steps. In order to identify the area of interest in investigated samples, we first create annotations for them which are later used for Faster-RCNN training. The Faster-RCNN model employs the VGG-19 network to extract the relevant keypoints from the given images which are later passed to the regressor and classification units to identify and categorize the various crop diseases using the estimated features. We evaluated our method on a widely used standard plant sample repository called the PlantVillage database, and the findings show that our approach is reliable for classifying plant diseases under a variety of image-capturing scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crops

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An IoT and Random Forest-DT-Based Early Enhanced Forest Fire Detection in Large and Small Targets

An IoT and Random Forest-DT-Based Early Enhanced Forest Fire...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Manjula Prabakaran Omesh Wadhwani V. Pandiaraj Kaneez Zainab Neerav Nishant Justin Z Department of CSE (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Information Technology Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer Science & Engineering B.N. College of Engineering and Technology Lucknow India Department of CSE School of Engineering Babu Banarasi Das University Lucknow India Department of Science and Humanities (English) RMK College of Engineering and Technology Thiruvalluvar India

The world is confronted with more complex and severe environmental concerns than in the past. Extreme weather events have become more common and destructive in the last several decades, endangering forests and urban forests around the globe. Massive forest fires are among the most catastrophic natural catastrophes that affect Earth’s climate and the diversity of life on it. Thus, in order to minimize harmful impacts on ecosystems and people, it is vital to have well-planned and coordinated plans for early warning, prevention, and reaction. Image preprocessing, feature extraction, and training the model are the initial steps in the suggested methodology. Histogram matching and picture smoothing are part of the image preparation procedure. The gray histogram is a way to see how many pixels fall into each grayscale level, and image smoothing is a way to enhance a large region of a picture using image processing. The GLCM and LBP histograms are employed in the feature extraction process. Training RF-Multivariate ARS models requires feature extraction as a prerequisite. The system improved accuracy to 97.29 percent after applying the approach.

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An Application of the Partially Observed Markov Process in the Analysis of Transmission Dynamics of COVID-19 via Wastewater

An Application of the Partially Observed Markov Process in t...

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2021 IEEE Systems and Information engineering Design Symposium, SIEDS 2021

作者： Jiang, Sihang Maggard, Kristen Shakeri, Heman Porter, Michael D. University of Virginia Department of Engineering Systems and Environment CharlottesvilleVA United States University of Virginia Department of Computer Science CharlottesvilleVA United States University of Virginia School of Data Science CharlottesvilleVA United States University of Virginia School of Data Science Department of Engineering Systems and Environment CharlottesvilleVA United States

ISBN: (纸本)9781665412506

As the ongoing outbreak of Coronavirus Disease 2019 (COVID-19) is severely affecting all over the world, analysis of the transmission of COVID-19 is of more and more interest. We focus on the application of compartmental models in the analysis of transmission of COVID-19 based on the detected viral load in wastewater and the reported number of cases. The measurement of COVID-19 RNA concentrations in primary sludge gives us information about the virus prevalence on a population level. Since the transmission of COVID-19 is a partially observed Markov process including different states, we consider a likelihood-based approach to our statistical inference to understand the inner relationship between different states and how COVID-19 actually transmits. Understanding the transmission dynamics of COVID-19 could give suggestions on public policies. © 2021 IEEE.

关键词： COVID-19

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Experience-Based Access Control in UbiComp: A New Paradigm

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Journal of computer and Communications 2022年第1期10卷 133-157页

作者： Nalini A. Mhetre Arvind V. Deshpande Parikshit N. Mahalle Department of Computer Engineering Sinhgad College of Engineering Pune India Department of Computer Engineering Smt. Kashibai Navale College of Engineering Pune India Department of AI and Data Science Vishwakarma Institute of Information Technology Pune India

Experience is a sociological concept and builds over time. In a broader sense, the human-centered equivalents of experience and trust apply to D2D interaction. Ubiquitous computing (UbiComp) embeds intelligence and computing capabilities in everyday objects to make them effectively communicate, share resources, and perform useful tasks. The safety of resources is a serious problem. As a result, authorization and access control in UbiComp is a significant challenge. Our work presents experience as an outcome of history (HI), reliability (RL), transitivity (TR), and Ubiquity (UB). This experience model is easily adaptable to a variety of self-regulating context-aware access control systems. This paper proposes a framework for Experience-Based Access Control (EX-BAC) with all major services provided by the model. EX-BAC extends attribute-based access control. It uses logical device type and experience as context parameters for policy design. When compared with the state-of-the-art, EX-BAC is efficient with respect to response time.

关键词： Access Control Experience-Based Access Control Experience Model History Reliability Transitivity Ubiquitous Computing Ubiquity

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FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource Allocation

arXiv

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arXiv 2024年

作者： Wang, Tianfu Fan, Qilin Wang, Chao Yang, Long Ding, Leilei Yuan, Nicholas Jing Xiong, Hui School of Computer Science and Technology University of Science and Technology of China China School of Big Data and Software Engineering Chongqing University China Guangzhou HKUST Fok Ying Tung Research Institute China School of Artificial Intelligence Peking University China Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong

Virtual network embedding (VNE) is an essential resource allocation task in network virtualization, aiming to map virtual network requests (VNRs) onto physical infrastructure. Reinforcement learning (RL) has recently emerged as a promising solution to this problem. However, existing RL-based VNE methods are limited by the unidirectional action design and one-size-fits-all training strategy, resulting in restricted searchability and generalizability. In this paper, we propose a FLexible And Generalizable RL framework for VNE, named FlagVNE. Specifically, we design a bidirectional action-based Markov decision process model that enables the joint selection of virtual and physical nodes, thus improving the exploration flexibility of solution space. To tackle the expansive and dynamic action space, we design a hierarchical decoder to generate adaptive action probability distributions and ensure high training efficiency. Furthermore, to overcome the generalization issue for varying VNR sizes, we propose a meta-RL-based training method with a curriculum scheduling strategy, facilitating specialized policy training for each VNR size. Finally, extensive experimental results show the effectiveness of FlagVNE across multiple key metrics. Our code is available at GitHub1 Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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USEFUSE: UTILE STRIDE FOR ENHANCED PERFORMANCE IN FUSED LAYER ARCHITECTURE OF DEEP NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Ibrahim, Muhammad Sohail Usman, Muhammad Lee, Jeong-A Department of Mechanical Systems Engineering Kumoh National Institute of Technology Gumi-Si Korea Republic of Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Department of Computer Engineering Chosun University Gwangju Korea Republic of

Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance overall performance. The study proposes a methodology for fusing multiple convolution layers to reduce off-chip memory communication and increase overall performance. An effective mechanism detects and skips inefficient convolutions after ReLU layers, minimizing power consumption without compromising accuracy. Furthermore, efficient tile movement guarantees uniform access to the fusion pyramid. An analysis demonstrates the utile stride strategy improves operational intensity. Two designs cater to varied demands: one focuses on minimal response time for mission-critical applications, and another focuses on resource-constrained devices with comparable latency. This approach notably reduced redundant computations, improving the efficiency of CNN deployment on edge devices. © 2024, CC BY.

关键词： Convolutional neural networks

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